Meta has introduced Muse Wonder and Muse Code, models designed for agentic tasks that can extend across documents, tools and time. The company says developers can access the models through its API and use Muse Code for software work, while examples include insurance claims, lease analysis and financial tasks.

The product concept relies on “Active Memory” and “Active Focus”, according to Meta. That makes context management a governance issue, not merely a convenience feature. Persistent memory can improve continuity, but it also creates questions about data provenance, retention, deletion, access rights and the possibility that an incorrect or sensitive detail shapes later actions.

Meta says the models were trained to avoid sharing private information and causing harm. It also says a safety report will be available soon. Until that report is published, customers should treat the release note as a description of intended safeguards rather than a complete assurance package.

The enterprise control checklist is straightforward: define what the agent may remember, separate short-lived task context from durable memory, give users a way to inspect and correct stored information, and test whether deletion propagates through indexes and downstream tools. Long-running agents need memory governance that is as explicit as their tool permissions.


Execution Level Governance- What audit-ready agent governance actually looks like
David Girvin, founder and CEO of Assury argues that model-in-the-loop review, AI governing AI, is fundamentally unreliable for regulated environments: even the best-performing models miss a meaningful share of violations, the reviewing model is typically provided by the same vendor being reviewed, and prompt injection or context poisoning can compromise both the acting agent and its supposed overseer simultaneously. He makes the case for deterministic, architecturally enforced controls instead, walking through Assury's approach of autonomy zones, session risk accumulation, and credential starvation, which lets a compromised agent be cut off from its tools instantly rather than relying on time-boxed access. The conversation touches on why David is sceptical of just-in-time credentialing as a solution for agent security more broadly, since agent sessions don't run on predictable human timescales, along with the current gap between how identity and security vendors are pitching agent protection and what he sees happening at the execution layer in practice. He also discusses the compliance and audit implications of probabilistic decision-making, arguing that regulated industries will increasingly need tamper-evident, hash-chained audit trails that can withstand scrutiny from auditors and regulators who are only beginning to understand agentic risk, and reflects on a named frontier lab's own published framework as an example of the gap between research and practitioner reality. Elsewhere, David reflects candidly on building a bootstrapped security company in an increasingly crowded market, why he turned down aggressive VC funding to stay in control of the product, and what a credible third-party assessment of his own gateway would need to look like given that Assury sits directly in the execution path for every customer's agents.
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